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Abstract Background Sex differences in language-related abilities have been reported. It is generally assumed that these differences stem from a different organization of language in the brains of females and males. However, research in this area has been relatively scarce, methodologically heterogeneous and has yielded conflicting results. Methods Univariate and multivariate sex differences and similarities in gray matter volume (GMVOL) within 18 essential language-processing brain areas were assessed in a sex-balanced sample (N = 588) of right-handed young adults. Univariate analyses involved location, spread, and shape comparisons of the females’ and males’ distributions and were conducted with several robust statistical methods able to quantify the size of sex differences and similarities in a complementary way. Multivariate sex differences and similarities were estimated by the same methods in the continuous scores provided by two distinct multivariate procedures (logistic regression and a multivariate analog of the Wilcoxon–Mann–Whitney test). Additional analyses were addressed to compare the outcomes of these two multivariate analytical strategies and described their structure (that is, the relative contribution of each brain area to the multivariate effects). Results When not adjusted for total intracranial volume (TIV) variation, “large” univariate sex differences (males > females) were found in all 18 brain areas considered. In contrast, “small” differences (females > males) in just two of these brain areas were found when controlling for TIV. The two multivariate methods tested provided very similar results. Multivariate sex differences surpassed univariate differences, yielding "large" differences indicative of larger volumes in males when calculated from raw GMVOL estimates. Conversely, when calculated from TIV-adjusted GMVOL, multivariate differences were "medium" and indicative of larger volumes in females. Despite their distinct size and direction, multivariate sex differences in raw and TIV-adjusted GMVOL shared a similar structure and allowed us to identify the components of the SENT_CORE network which more likely contribute to the observed effects. Conclusions Our results confirm and extend previous findings about univariate sex differences in language-processing areas, offering unprecedented evidence at the multivariate level. We also observed that the size and direction of these differences vary quite substantially depending on whether they are estimated from raw or TIV-adjusted GMVOL measurements.

Based on Social Cognitive Career Theory principles, the present study sought to investigate whether stereotype threat experiences could act as a barrier and reduce the persistence of women in math-intensive activities. More specifically, we assessed whether the experimental activation of stereotypes about women’s lower math capabilities affected the performance, persistence, and self-selected difficulty of engineering students in a math task which required sustained effort. We also evaluated the relationships between these effects and the participants’ pre-testing gender-science stereotypes and math self-concept. A sample of 340 engineering students (175 and 165 self-identified as males and females, respectively) were recruited and randomly assigned to a control (Con) or stereotype threat (StA) condition to form four similarly sized groups. All participants rated their self-concept in four academic domains (math, chemistry, physics, and coding), completed the gender-science Implicit Association Test, and a “reading comprehension task” that served to promote the stereotype threat manipulation immediately before facing a modified version of the Math Effort Task (M-MET). We observed that, in the control condition, M-MET performance, self-selected difficulty, and persistence were similar in female and male participants, independent of their gender-science implicit stereotypes but correlated to their math self-concept scores. In contrast, the StA condition triggered opposite effects in female and male students, so they showed decreased/enhanced M-MET performance and self-selected difficulty, respectively. This experimental condition also resulted in enhanced persistence of the male students without affecting the number of trials completed by female students. These effects were correlated with the strength of the participants’ gender-science implicit stereotypes but not with their math self-concept scores. In fact, as revealed by finer-grain analyses, stereotype threat only had a significant impact on individuals harboring stereotypical gender-science implicit associations. Therefore, it is concluded that: (1) stereotypes can promote differences between male and female engineering students in their performance, self-confidence, and persistence in math-related activities; (2) These effects seem to be more prominent in individuals implicitly perceiving science as a masculine domain. The relevance of these findings to explain women’s enhanced abandonment rates of math-intensive studies is discussed.

Previous studies have shown that machine-learning (ML) algorithms can “predict” sex based on brain anatomical/ functional features. The high classification accuracy achieved by ML algorithms is often interpreted as revealing large differences between the brains of males and females and as confirming the existence of “male/female brains”. However, classification and estimation are different concepts, and using classification metrics as surrogate estimates of between-group differences may result in major statistical and interpretative distortions. The present study avoids these distortions and provides a novel and detailed assessment of multivariate sex differences in gray matter volume (GMVOL) that does not rely on classification metrics. Moreover, appropriate regression methods were used to identify the brain areas that contribute the most to these multivariate differences, and clustering techniques and analyses of similarities (ANOSIM) were employed to empirically assess whether they assemble into two sex-typical profiles. Results revealed that multivariate sex differences in GMVOL: (1) are “large” if not adjusted for total intracranial volume (TIV) variation, but “small” when controlling for this variable; (2) differ in size between individuals and also depends on the ML algorithm used for their calculation (3) do not stem from two sex-typical profiles, and so describing them in terms of “male/female brains” is misleading.